2493. Data-Driven Approaches to Financial Inclusion: The use of Cluster Analysis for Enhancing Newcomer Families' Financial Well-Being in Canada
Invited abstract in session TD-28: Risk and Financial Decision Making, stream Decision Support Systems.
Tuesday, 14:30-16:00Room: Maurice Keyworth 1.03
Authors (first author is the speaker)
| 1. | Antonia Gieschen
|
| University of Edinburgh | |
| 2. | Avneet Bhabra
|
| Scotiabank | |
| 3. | Khushi Toprani
|
| McGill University | |
| 4. | catherine paquet
|
| Marketing, Universite Laval | |
| 5. | Laurette Dubé
|
| McGill University |
Abstract
Financial inclusion describes the act of widening access to financial products and services for diverse people and businesses. In order to support vulnerable populations in achieving this goal, understanding their unique characteristics and financial situation is crucial. In this study, we present a data-driven approach to improving the financial inclusion of immigrant families in Canada through three studies. Leveraging unsupervised machine learning, specifically hierarchical clustering and Partitioning Around Medoids (PAM) clustering, we analysed financial wellbeing survey data from the Financial Consumer Agency of Canada (FCAC) between 2019 and 2022 to identify distinct financial behaviour and spatial location patterns among immigrants. The presented analysis will build on our previous work in defining cluster-driven personas to allow policy makers to personalise financial inclusion support and interventions. We utilised these personas and their geospatial pattern to demonstrate their use in localised policy making. In subsequent research, we statistically analyse the differences in subjective (perceived) financial wellbeing between immigrant women and men, as well as between individuals at different stages in their life. Through discussion of our findings, we demonstrate that tailored, data-driven interventions can significantly enhance the financial literacy and wellbeing of immigrant families, contributing to broader financial inclusion goals in Canada.
Keywords
- Machine Learning
- Finance and Banking
- OR/MS and the Public Sector
Status: accepted
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